Analog VLSI microchips for early vision are the focus of an active research area. These chips, which have self-contained photoreceptor arrays, process large amounts of data in parallel to extract certain features of the images. This data compression greatly reduces the information bandwidth of the output because only important aspects of the image, rather than the entire image, are needed for many early vision tasks. Analog implementation schemes are promising because of the nature of such tasks--very large numbers of arithmetic operations are required, which is a bottleneck for digital schemes, yet they are tolerant to errors in analog computation.
The present invention addresses the need for analog VLSI microchips that determine the position and orientation of an object. Presently, most commercially available machine vision systems have only rudimentary mechanisms for dealing with grey-level images and are aimed mainly at binary images. These systems typically have digital means for computing the moments required to determine position and orientation. While such systems are restricted in their application, they are widely available and well understood. They can be used, for example, to determine the position and orientation of an isolated, contrasting workpiece lying flat on a conveyor belt (see, for example, Chapter 3 in Robot Vision by B. K. P. Horn, MIT Press, 1986). Once the position and orientation of the object are known, a robot hand with the appropriate orientation may be sent to the indicated position to pick up the part. A device that finds the centroid of a spot of light in the image can also be used as a high-resolution light-pen and a means of tracking a light source, such as a light bulb attached to an industrial robot arm.
A variety of methods is available for efficiently computing the zeroth- and first-order moments, which can be used to indicate the position of an object. Less appears to be known about how to easily compute second- and higher-order moments, which can be used to indicate the orientation of an object. This subject is discussed further in "Parallel Networks for Machine Vision" by B. K. P. Horn (Massachusetts Institute of Technology Artificial Intelligence Laboratory Memo 1071, December 1988). The present invention discloses a fast and elegant method for computing position and orientation of an object by moment extraction using analog networks of relatively few components.